Computational Privacy Group

We are a young research group at Imperial College London studying the privacy risks arising from large scale behavioral datasets. We develop attack models and design solutions to collect and use data safely.

Today people leave digital breadcrumbs wherever they go and whatever they do online and offline. This data dramatically increases our capacity to understand and affect the behavior of individuals and collectives, has been key to recent advances in AI, but also raises fundamentally new privacy and fairness questions. The Computational Privacy Group aims to provide leadership, in the UK and beyond, in the safe, anonymous, and ethical use of large-scale behavioral datasets coming from the Internet of Things (IoT) devices, mobile phones, credit cards, browsers, etc.

Our projects have already demonstrated the limits of data anonymization (or de-identification) in effectively protecting the privacy of individuals in Big Data, the risk of inference in behavioral datasets coming from mobile phone, and developed solutions to allow individuals and companies to share data safely. While technical in nature, our work has had significant public policy implication for instance in reports of United Nations, FTC, and the European Commission as well as in briefs to the U.S. Supreme Court.

Research Areas

Identification Learning

We develop statistical and machine learning techniques to uniquely identify individuals in large-scale behavioral datasets. These techniques show the limits of pseudonymization and anonymization in protecting people's privacy.

Safe Data Sharing

We build privacy-preserving and conscientious techniques to collect and use data while respecting people's privacy. For instance, we're building with MIT the OPAL (Open Algorithms) platform to safely share location data and openPDS to give individuals control over their data.

Societal impact of AI

Modern privacy is not only about controlling the information but also the ability to control how this information is used e.g. for insurance pricing or ad-targeting. We study fairness in algorithmic-decision making and, more generally, the impact of AI on society.

News and Events

    CPG at CNIL Privacy Research Day
    Jul 14, 2023

    The CPG attended the CNIL Privacy Research Day in Paris in June 2023. Ana-Maria Crețu presented her paper on automated privacy attacks (Querysnout), Shubham Jain presented both papers on perceptual hashing and Florent Guépin presented his paper on correlation inference attacks.

    CPG at ACM CCS 2022
    Nov 10, 2022

    Ana-Maria Cretu and CPG alumnus Florimond Houssiau (currently a postdoc at The Alan Turing Institute) presented their paper “QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems” at the ACM CCS 2022 conference in Los Angeles.

    New paper “Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral data” published in Science Advances
    Aug 19, 2022

    In a new paper published in Science Advances, Arnaud J. Tournier and Yves-Alexandre de Montjoye propose an entropy-based profiling attack for location data which shows that much more auxiliary information than previously believed is available to re-identify individuals in …

    More News

Selected publications

The full list of our papers is available on Google Scholar.

Our Team


Email:X@Y where X=demontjoye,
Administrator (if urgent): Amandeep Bahia, +44 20 7594 8612

We are located at the Data Science Institute in the William Penney Laboratory. The best entry point is via Exhibition road, through the Business school (see map below). From there, just take the stairs towards the outdoor court. Enter the outdoor corridor after the court and the institute will be on your right (please press the Data Science intercom button for access).

Please address mails to:
Department of Computing
Imperial College London
180 Queens Gate
London SW7 2AZ